Statistical Characterization of Wireless Interference Signal Based On UWB Spectrum Sensing

Ramoni Ojekunle Adeogun; Gilberto Berardinelli; Preben E. Mogensen; Ignacio Rodriguez

doi:10.1109/VTC2020-Spring48590.2020.9128857

Statistical Characterization of Wireless Interference Signal Based On UWB Spectrum Sensing

Ramoni Ojekunle Adeogun, Gilberto Berardinelli, Preben E. Mogensen, Ignacio Rodriguez

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

2 Citations (Scopus)

135 Downloads (Pure)

Abstract

Ultra-wideband (UWB) technology offers the potential for unparalleled support of short-range broadband communication over a multi-gigahertz spectrum and are expected to enable several applications with extreme requirements in future wireless networks. Enabling these systems in the unlicensed spectrum requires efficient co-existence management and adequate understanding of the characteristics and spatio-temporal dynamics of interference signals over the multi-GHz bandwidth. This paper investigates the suitability of Gaussian, Middleton canonical class A, symmetric alpha stable and Gaussian Mixture distributions for modelling radio frequency interference from systems in the UWB spectrum based on measurements. We evaluate the closeness of fit of the distributions to measured interference data and provide insights on the applicability of these models for characterizing interference in the UWB spectrum. Results show that the Gaussian Mixture distribution (GMD) yielded the best fit to the measured interference evaluated with Kullback-Leibler (KL) divergence below 0.05. Results also show that interference signals generated from the GMD agree closely with the measurements.

Original language	English
Title of host publication	2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)
Number of pages	5
Publisher	IEEE
Publication date	30 Jun 2020
Article number	9128857
ISBN (Print)	978-1-7281-4053-7
ISBN (Electronic)	978-1-7281-5207-3
DOIs	https://doi.org/10.1109/VTC2020-Spring48590.2020.9128857
Publication status	Published - 30 Jun 2020
Event	2020 IEEE 91st Vehicular Technology Conference - Antwerpen, Belgium Duration: 25 May 2020 → 28 May 2020

Conference

Conference	2020 IEEE 91st Vehicular Technology Conference
Country/Territory	Belgium
City	Antwerpen
Period	25/05/2020 → 28/05/2020

Series	IEEE VTS Vehicular Technology Conference Proceedings
ISSN	1090-3038

Keywords

Gaussian mixture model
Interference measurements
Interference modelling
UWB
statistical distributions
symmetric alpha stable distribution

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10.1109/VTC2020-Spring48590.2020.9128857

RFIStatisticalModelsAccepted author manuscript, 8.3 MB

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@inproceedings{2aa9b9cc53af4bac952ebfe0e89f14f5,

title = "Statistical Characterization of Wireless Interference Signal Based On UWB Spectrum Sensing",

abstract = "Ultra-wideband (UWB) technology offers the potential for unparalleled support of short-range broadband communication over a multi-gigahertz spectrum and are expected to enable several applications with extreme requirements in future wireless networks. Enabling these systems in the unlicensed spectrum requires efficient co-existence management and adequate understanding of the characteristics and spatio-temporal dynamics of interference signals over the multi-GHz bandwidth. This paper investigates the suitability of Gaussian, Middleton canonical class A, symmetric alpha stable and Gaussian Mixture distributions for modelling radio frequency interference from systems in the UWB spectrum based on measurements. We evaluate the closeness of fit of the distributions to measured interference data and provide insights on the applicability of these models for characterizing interference in the UWB spectrum. Results show that the Gaussian Mixture distribution (GMD) yielded the best fit to the measured interference evaluated with Kullback-Leibler (KL) divergence below 0.05. Results also show that interference signals generated from the GMD agree closely with the measurements. ",

keywords = "Gaussian mixture model, Interference measurements, Interference modelling, UWB, statistical distributions, symmetric alpha stable distribution",

author = "Adeogun, {Ramoni Ojekunle} and Gilberto Berardinelli and {E. Mogensen}, Preben and Ignacio Rodriguez",

year = "2020",

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booktitle = "2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)",

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Adeogun, RO , Berardinelli, G , E. Mogensen, P & Rodriguez, I 2020, Statistical Characterization of Wireless Interference Signal Based On UWB Spectrum Sensing. in 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)., 9128857, IEEE, IEEE VTS Vehicular Technology Conference Proceedings, 2020 IEEE 91st Vehicular Technology Conference , Antwerpen, Belgium, 25/05/2020. https://doi.org/10.1109/VTC2020-Spring48590.2020.9128857

Statistical Characterization of Wireless Interference Signal Based On UWB Spectrum Sensing. / Adeogun, Ramoni Ojekunle ; Berardinelli, Gilberto ; E. Mogensen, Preben et al.
2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring). IEEE, 2020. 9128857 (IEEE VTS Vehicular Technology Conference Proceedings).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

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AU - E. Mogensen, Preben

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N2 - Ultra-wideband (UWB) technology offers the potential for unparalleled support of short-range broadband communication over a multi-gigahertz spectrum and are expected to enable several applications with extreme requirements in future wireless networks. Enabling these systems in the unlicensed spectrum requires efficient co-existence management and adequate understanding of the characteristics and spatio-temporal dynamics of interference signals over the multi-GHz bandwidth. This paper investigates the suitability of Gaussian, Middleton canonical class A, symmetric alpha stable and Gaussian Mixture distributions for modelling radio frequency interference from systems in the UWB spectrum based on measurements. We evaluate the closeness of fit of the distributions to measured interference data and provide insights on the applicability of these models for characterizing interference in the UWB spectrum. Results show that the Gaussian Mixture distribution (GMD) yielded the best fit to the measured interference evaluated with Kullback-Leibler (KL) divergence below 0.05. Results also show that interference signals generated from the GMD agree closely with the measurements.

AB - Ultra-wideband (UWB) technology offers the potential for unparalleled support of short-range broadband communication over a multi-gigahertz spectrum and are expected to enable several applications with extreme requirements in future wireless networks. Enabling these systems in the unlicensed spectrum requires efficient co-existence management and adequate understanding of the characteristics and spatio-temporal dynamics of interference signals over the multi-GHz bandwidth. This paper investigates the suitability of Gaussian, Middleton canonical class A, symmetric alpha stable and Gaussian Mixture distributions for modelling radio frequency interference from systems in the UWB spectrum based on measurements. We evaluate the closeness of fit of the distributions to measured interference data and provide insights on the applicability of these models for characterizing interference in the UWB spectrum. Results show that the Gaussian Mixture distribution (GMD) yielded the best fit to the measured interference evaluated with Kullback-Leibler (KL) divergence below 0.05. Results also show that interference signals generated from the GMD agree closely with the measurements.

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Statistical Characterization of Wireless Interference Signal Based On UWB Spectrum Sensing

Abstract

Conference

Keywords

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Enabling ultra-reliable low-latency communication in wireless networks via interference prediction

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Statistical Characterization of Wireless Interference Signal Based On UWB Spectrum Sensing

Abstract

Conference

Keywords

Access to Document

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Projects

Enabling ultra-reliable low-latency communication in wireless networks via interference prediction

Cite this